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1. Evaluation of an early flood warning system in Bamako (Mali): Lessons learned from the flood of May 2019

Author

Nanée Chahinian, Matias Alcoba, Ndji dit Jacques Dembélé, Fréderic Cazenave, and Christophe Bouvier

Subjects
early warning systems, experience feedback (EF), flash flood, forecasting and warning, hydrological modelling, pluvial flooding, River protective works. Regulation. Flood control, TC530-537, Disasters and engineering, TA495
Abstract

Abstract Devastating floods have plagued many West African cities in the past decades. In an attempt to reduce flood damage in Bamako (Mali), an early warning system (EWS) demonstrator (Raincell App) was developed for flash floods. On 16 May 2019, while the demonstrator was partially operational, an intense rainfall event led to devastating floods. We carried out an experience feedback on this flood event by comparing EWS simulations to the results of a field survey. Given the synoptic situation and the rapid development pattern of the storm, none of the global forecasting systems were able to foresee its occurrence and magnitude. The hydrological model developed as part of the demonstrator correctly identified most of the locations where overbank flow occurred. In the absence of data, the predicted discharge and volume values could not be validated. However, they are realistic based on the water levels reported in the Post‐Disaster Needs Assessment report. It would be advisable to couple it to a two‐dimensional hydraulic model and add discharge and water level monitoring to the already existing rainfall surveillance scheme to further improve the system's performance. Increasing the local population's awareness of the dangers of clogged waterways is also mandatory.

Published
2023
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2. Network representation in hydrological modelling on urban catchments in data-scarce contexts: A case study on the Oued Fez catchment (Morocco)

Author

Ismail Bouizrou, Nanée Chahinian, Jean-Louis Perrin, Rémi Müller, and Naoual Rais

Subjects
Hydrological modelling, Flood events, Urbanisation, Distributed hydrological models, ATHYS, Oued Fez catchment, Physical geography, GB3-5030, Geology, QE1-996.5
Abstract

Study region: Oued Fez, Morocco. Study focus: Urban catchments are heterogeneous in terms of land use and have both natural and artificial drainage networks. Modelling them is not a straightforward task especially in data-scarce settings. This study investigates network representation in hydrological modelling using field data collected between 2008 and 2018 on the Oued Fez catchment. The road network is used as a proxy for the stormwater network. Two production functions, SCS (1972) and a linear reservoir, are coupled to the lag and route transfer function. Three types of land use classes are used. Tests are carried out at hourly and 5-minute time steps using both the natural and modified drainage networks. New hydrological insights for the region: Fifty-three rainfall–runoff events are monitored on the urban part of the catchment over the 2008–2018 period. The highest rainfall values are recorded in 2008/2009, while the highest peak flow values are recorded in 2017/2018. This is due to stream channelisation and increased stormwater network coverage. Using the road network with minimal land use classes improves the model performance at both the hourly and 5-minute time steps.

Published
2021
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5. Assessing the effects of urbanization on flood events using hydrological modelling: A case study of the Oued Fez urban catchment (Morocco)

Author

Ismail Bouizrou, Nanée Chahinian, Jean-Louis Perrin, Remy Müller, and Naoual Rais

Abstract

Urban catchments are very complex systems due to urban development. In these contexts, the intense urban expansion induces local changes in the hydrological cycle and increases flood risk by extending impervious surfaces and altering the natural watercourses. Therefore, modelling them is a challenging task specially in data-sparse regions. Indeed, a compromise has to be reached to take into account the heterogeneity while avoiding over parameterisation. The Oued Fez catchment located in the northern part of Morocco is a typical example of urban catchments: its land cover is highly heterogeneous and has channelised water courses. In this work, the ATHYS modelling platform is used to simulate 59 selected flood events monitored on the urban and peri-urban parts of the Oued Fez catchment over the 2008-2018 periods. Two production functions, SCS (Soil Conservation Service) and a linear reservoir are combined with the lag and route transfer function. The drainage network is derived based on a high-resolution Digital Elevation Model (Source Dem from ALOS PALSAR RTC, https://asf,alaska,edu/datasets/sar-data-sets/alos-palsar/, 12,5m resolution). The road network is used as a proxy of the stormwater network to force the drainage directions along the motorways and three types of land use classes are used to parameterize the two production functions. Tests are carried out at hourly and sub-hourly time steps using both the natural and modified drainage networks. The main results show that using the road network and minimal land use classes improves model performance. The field data also shows that despite lower rainfall values, the highest peak flow values are recorded in 2017/2018. This is due to stream channelisation and increased stormwater network coverage.Keywords: Hydrological modelling; Flood events; Urbanization; Distributed hydrological models; ATHYS, Data-scarce regions ; Oued Fez catchment.

Published
2022
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6. Parametrization of a wastewater hydraulic model under incomplete data constraint

Author

Yassine Belghaddar, Carole Delenne, Nanée Chahinian, Abderrahmane Seriai, Ahlame Begdouri, Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Laboratoires Systèmes Intelligents et Applications (LSIA), Université Sidi Mohamed Ben Abdellah (USMBA), Berger-Levrault, Littoral, Environment: MOdels and Numerics (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Hydrosciences Montpellier (HSM), and Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
missing data, SWMM, Wastewater network, hydraulic simulation, General Medicine, General Chemistry, parameter estimation, [SDE.ES]Environmental Sciences/Environmental and Society
Abstract

Hydraulic simulation represents a powerful tool for studying wastewater networks. In order to achieve this target, hydraulic software require a set of parameters such as pipe slopes, roughness, diameters, etc. However, these pieces of information are rarely known for each and every pipe. Moreover, underground networks are frequently expanded, repaired and improved and these changes are not always reported in databases. The task of completing the required data represents the most time-consuming part of model implementation. In this context, we present algorithms that complete missing data required by hydraulic software. We automated this data insertion and transformation in SWMM© format to make it quicker and easier for the user. This automated solution was compared with manually estimated inputs. The simulation results show a coherent hydraulic behaviour.

Published
2022
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7. Towards a generic fusion framework for underground networks involving model-driven engineering domain

Author

Yassine Belghaddar, Abderrahmane Seriai, Ahlame BEGDOURI, Carole Delenne, Nanée Chahinian, Rima Bachar, Mustapha Derras, Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Littoral, Environment: MOdels and Numerics (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Berger-Levrault, Laboratoires Systèmes Intelligents et Applications (LSIA), and Université Sidi Mohamed Ben Abdellah (USMBA)

Subjects
[INFO]Computer Science [cs], [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology
Abstract

Underground networks, particularly sewerage networks require accurate information for their management. To successfully and smoothly accomplish the required tasks for network expansion, reparation or simulation analysis, operators collect and analyse data coming from multiple sources. The various and heterogeneous sources available often provide different representations for a network. Thus, raising challenges for information exploitation and communication between the different actors managing these networks. In addition, the imperfections related to the sources are numerous and their consideration in the decision making process is mandatory. In this paper, we propose a generic data modelling for the fusion of sewerage networks data. Our meta-model supports imperfection modelling at data-source level as well as at network object position and attribute levels, allowing thus formal fusion operations to be conducted efficiently and reliably. To validate our meta-model, we implemented it using data analysis and reengineering platform called Moose, and we conducted a test on the town of Prades-le-Lez (France). We took into account three data-sources providing information on the node positions of the sewerage network : 1- the official network map as semi-structured source, 2- a high resolution aerial image database and 3- a Google Street View database as unstructured sources. As result, we were able to reliably perform data monitoring and visualization requests on real heterogeneous multi-source data related to a specific sewerage network., International Journal of Information Science and Technology, Vol 6 No 2 (2022)

Published
2022
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8. Graph Convolutional Networks: Application to Database Completion of Wastewater Networks

Author

Nanée Chahinian, Reda Abdou, Yassine Bel-Ghaddar, Abderrahmane Seriai, Ahlame Begdouri, Carole Delenne, Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Laboratoires Systèmes Intelligents et Applications (LSIA), Université Sidi Mohamed Ben Abdellah (USMBA), Berger-Levrault, Littoral, Environment: MOdels and Numerics (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environnement : Méthodes et Outils Numériques (LEMON), and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
Service (systems architecture), Exploit, Computer science, Process (engineering), Geography, Planning and Development, graph neural network, Topology (electrical circuits), 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Aquatic Science, computer.software_genre, Biochemistry, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, TD201-500, Water Science and Technology, Structure (mathematical logic), Water supply for domestic and industrial purposes, Database, Hydraulic engineering, Missing data, missing value imputation, 6. Clean water, machine learning, Wastewater, Graph (abstract data type), 020201 artificial intelligence & image processing, wastewater network, TC1-978, computer
Abstract

International audience; Wastewater networks are mandatory for urbanisation. Their management, including the prediction and planning of repairs and expansion operations, requires precise information on their underground components (manhole covers, equipment, nodes, and pipes). However, due to their years of service and to the increasing number of maintenance operations they may have undergone over time, the attributes and characteristics associated with the various objects constituting a network are not all available at a given time. This is partly because (i) the multiple actors that carry out repairs and extensions are not necessarily the operators who ensure the continuous functioning of the network, and (ii) the undertaken changes are not properly tracked and reported. Therefore, databases related to wastewater networks may suffer from missing data. To overcome this problem, we aim to exploit the structure of wastewater networks in the learning process of machine learning approaches, using topology and the relationship between components, to complete the missing values of pipes. Our results show that Graph Convolutional Network (GCN) models yield better results than classical methods and represent a useful tool for missing data completion.

Published
2021
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9. Rainfall measurement from Commercial microwave links for urban hydrology in Africa: a simulation framework for sensitivity analysis

Author

Marielle Gosset, Matias Alcoba, Christophe Bouvier, Nanée Chahinian, Maxime Turko, Modeste Kacou, Aaron Boone, Géosciences Environnement Toulouse (GET), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD), Laboratoire de Physique de l'Atmosphère [Abidjan] (LAPA), Université de Cocody, Hydrosciences Montpellier (HSM), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique de l'Atmosphère et de Mécanique des Fluides (LPA-MF), Université Félix Houphouët-Boigny (UFHB), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), and Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)

Subjects
Rainfall, Atmospheric Science, Hydrologic models, 010504 meteorology & atmospheric sciences, Meteorology, 0207 environmental engineering, 02 engineering and technology, Urban area, 01 natural sciences, law.invention, Operator (computer programming), law, Sensitivity (control systems), Precipitation, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, 020701 environmental engineering, 0105 earth and related environmental sciences, [PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph], geography, Microwave observations, geography.geographical_feature_category, Attenuation, Tropics, Uncertainty, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 6. Clean water, Current (stream), 13. Climate action, Environmental science, Weather radar, Microwave
Abstract

Urban floods due to intense precipitation are a major problem in many tropical regions as in Africa. Rainfall measurement using microwave links from cellular communication networks has been proposed as a cost effective solution to monitor rainfall in these areas where the gauge network is scarce. The method consists in retrieving rainfall from the attenuation estimated along the commercial microwave links (CMLs) thanks to the power levels provided by an operator. In urban areas where the network is dense, rainfall can be estimated and mapped for hydrological prediction. Rainfall estimation from CMLs is subject to uncertainties. This paper analyzes the advantages and limitations of this rainfall data for a distributed hydrological model applied to an urban area. The case study is in West Africa in Ouagadougou where a hydrological model has been set up. The analysis is based on numerical simulations, using high resolution rain maps from a weather radar to emulate synthetic microwave links. Two sources of uncertainty in the rain estimation and on the simulated discharge are analyzed by simulations: i) the precision of the raw information provided by the operator and ii) the density and geometry of the network. A coarse precision (1 dB) in the signal provided by the operator can lead to substantial underestimation of rainfall and discharge, especially for links operating at low frequency (below 10 GHz) or short (less than 1 km). The density of the current mobile networks in urban areas is appropriate to analyze hydrological impact of tropical convective rainfall.

Published
2021
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10. Missing data completion in wastewater network databases: the added-value of Graph Convolutional Neural Networks

Author

Abderrahmane Seriai, Carole Delenne, Nanée Chahinian, Yassine Bel-Ghaddar, Ahlame Begdouri, Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Laboratoires Systèmes Intelligents et Applications (LSIA), Université Sidi Mohamed Ben Abdellah (USMBA), Berger-Levrault, Littoral, Environment: MOdels and Numerics (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environnement : Méthodes et Outils Numériques (LEMON), and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
Computer science, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], computer.software_genre, Missing data, Convolutional neural network, 6. Clean water, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Wastewater, Added value, Graph (abstract data type), [INFO]Computer Science [cs], Data mining, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, computer, ComputingMilieux_MISCELLANEOUS
Abstract

Wastewater networks are mandatory for urbanization. Their management, which includes reparation and expansion operations, requires precise information about their underground components, mainly pipes. For hydraulic modelling purposes, the characteristics of the nodes and pipes in the model must be fully known via specific, complete and consistent attribute tables. However, due to years of service and interventions by different actors, information about the attributes and characteristics associated with the various objects constituting a network are not properly tracked and reported. Therefore, databases related to wastewater networks, when available, still suffer from a large amount of missing data.A wastewater network constitutes a graph composed of nodes and edges. Nodes represent manholes, equipment, repairs, etc. while edges represent pipes. Each of the nodes and edges has a set of properties in the form of attributes such as diameters of the pipes. In this work, we seek to complete the missing attributes of wastewater networks using machine learning techniques. The main goal is to make use of the graph structures in the learning process, taking into consideration the topology and the relationships between their components (nodes and edges) to predict missing attribute values.Graph Convolutional Network models (GCN) have gained a lot of attention in recent years and achieved state of the art in many applications such as chemistry. These models are applied directly on graphs to perform diverse machine learning tasks. We present here the use of GCN models such as ChebConv to complete the missing attribute values of two datasets (1239 and 754 elements) extracted from the wastewater networks of Montpellier and Angers Metropolis in France. To emphasize the importance of the graph structure in the learning process and thus on the quality of the predictions, GCNs' results are benchmarked against non-topological neural networks. The application on diameter value completion, indicates that using the structure of the wastewater network in the learning process has a significant impact on the prediction results especially for minority classes. Indeed, the diameter classes are very heterogeneous in terms of number of elements with a highly majority class and several classes with few elements. Non-topological neural networks always fail to predict these classes and affect the majority class value to every missing diameter, yielding a perfect precision for this class but a null one for all the others. On the contrary, the ChebConv model precision is slightly lower (0.93) for the majority class but much higher (increases from 0.3 to 0.81) for other classes, using only the structure of the graphs. The use of other available information in the learning process may enhance these results.

Published
2021
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11. WEIR-P: An Information Extraction Pipeline for the Wastewater Domain

Author

Marin Julien, Maguelonne Teisseire, Laurent Deruelle, Francesca Frontini, Carole Delenne, Lucile Sautot, Rachel Panckhurst, Mathieu Roche, Nanée Chahinian, Thierry Bonnabaud La Bruyère, Serge Conrad, Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environment: MOdels and Numerics (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Common Language Resources and Technology Infrastructure - European Research Infrastructure Consortium (CLARIN ERIC), Laboratoire de sociolinguistique, d’anthropologie des pratiques langagières et de didactique des langues-cultures (DIPRALANG), Université Paul-Valéry - Montpellier 3 (UPVM), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Berger-Levrault, Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environnement : Méthodes et Outils Numériques (LEMON), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Laboratoire de linguistique diachronique, de sociolinguistique et de didactique des langues (DIPRALANG)

Subjects
Petroleum engineering, Text mining, Information extraction, Computer science, Pipeline (computing), Wastewater, Natural language Processing, computer.software_genre, 6. Clean water, Domain (software engineering), Weir, [SDE]Environmental Sciences, [INFO]Computer Science [cs], computer, ComputingMilieux_MISCELLANEOUS
Abstract

Urbanization has been an increasing trend over the past century (UN, 2018) and city managers have had to constantly extend water access and sanitation services to new peripheral areas. Originally these networks were installed, operated, and repaired by their owners (Rogers et al. 2012). However, as concessions were increasingly granted to private companies and new tenders requested regularly by public authorities, archives were sometimes misplaced and event logs were lost. Thus, part of the networks’ operational history was thought to be permanently erased. The advent of Web big data and text-mining techniques may offer the possibility of recovering some of this knowledge by crawling secondary information sources, i.e. documents available on the Web. Thus, insight might be gained on the wastewater collection scheme, the treatment processes, the network’s geometry and events (accidents, shortages) which may have affected these facilities and amenities. The primary aim of the "Megadata, Linked Data and Data Mining for Wastewater Networks" (MeDo) project (http://webmedo.msem.univ-montp2.fr/?page_id=223&lang=en), is to develop resources for text mining and information extraction in the wastewater domain. We developed a specific Natural Language Processing (NLP) pipeline named WEIR-P (WastewatEr InfoRmation extraction Platform) which allows users to retrieve relevant documents for a given network, process them to extract potentially new information, assess this information also by using an interactive visualization and add it to a pre-existing knowledge base. The system identifies the entities and relations to be extracted from texts, pertaining network information, wastewater treatment, accidents and works, organizations, spatio-temporal information, measures and water quality. We present and evaluate the first version of the NLP system. The preliminary results obtained on the Montpellier corpus (1,557 HTML and PDF documents in French) are encouraging and show how a mix of Machine Learning approaches and rule-based techniques can be used to extract useful information and reconstruct the various phases of the extension of a given wastewater network. While the NLP and Information Extraction (IE) methods used are state of the art, the novelty of our work lies in their adaptation to the domain, and in particular in the wastewater management conceptual model, which defines the relations between entities.

Published
2021
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12. Combining model-driven engineering and sewerage networks: towards a generic representation

Author

Ahlame Begdouri, Abderrahmane Seriai, Carole Delenne, Mustapha Derras, Nanée Chahinian, Yassine Bel-Ghaddar, Berger-Levrault, Littoral, Environnement : Méthodes et Outils Numériques (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Hydrosciences Montpellier (HSM), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environment: MOdels and Numerics (LEMON), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), and Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Digital mapping, Computer science, Digital data, 020207 software engineering, 02 engineering and technology, computer.software_genre, Sensor fusion, [SDE.ES]Environmental Sciences/Environmental and Society, Data science, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Data modeling, Sewerage, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Model-driven architecture, Representation (mathematics), computer, Data integration, computer.programming_language
Abstract

International audience; Representing and processing digital data related to underground networks, particularly sewerage networks, is increasingly becoming a priorityfor the managers of these networks. Indeed better representation would allow them, among others, to improve knowledge and to take the best decisions regarding these generally poorly identified infrastructures. The heterogeneity of data and the multiplicity of data models representing sewerage networks, often specific to each operator, as well as the imperfections associated with both the available data and those collected from different sources, generate complexity in terms of on-the-field interventions efficiency. They also highlight the need for aggregation (unification),control and analysis. The main objective of our work is to merge multisource data to obtain more precise and complete digital maps of sewerage networks. In this paper, we propose a generic data modelling for dataf usion purposes taking into consideration the uncertainty aspects related to the collected data by allowing a confidence value for each data source and for each single data provided by a source.

Published
2020
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13. Automatic mapping of urban wastewater networks based on manhole cover locations

Author

Benjamin Commandré, Laurent Deruelle, Jean-Stéphane Bailly, Nanée Chahinian, Mustapha Derras, Carole Delenne, Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environment: MOdels and Numerics (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Berger-Levrault, AgroParisTech, Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), European Regional Development Fund (ERDF) European Union (EU), Languedoc Roussillon RegionRegion Occitanie, Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environnement : Méthodes et Outils Numériques (LEMON), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut de Recherche pour le Développement (IRD [ Madagascar])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
Cover (telecommunications), Computer science, Geography, Planning and Development, 0211 other engineering and technologies, Topology (electrical circuits), 02 engineering and technology, Wastewater, computer.software_genre, Set (abstract data type), Integrated management, ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering, General Environmental Science, [SDE.IE]Environmental Sciences/Environmental Engineering, Ecological Modeling, Multigraph, Elevation, 021107 urban & regional planning, 6. Clean water, Urban Studies, Network connectivity, Mapping, Urban hydrology, Graph (abstract data type), Data mining, computer, Street network
Abstract

International audience; Accurate maps of wastewater networks in cities are mandatory for an integrated management of water resources. However, in many countries around the world this information is unavailable or inaccurate. A new mapping method is put forward to create maps using manhole cover locations which could be available via ground surveys, remote sensing techniques or stakeholder's databases. A new algorithm is developed which considers manhole covers as the nodes of the network and connects them automatically by minimizing cost functions defined by industry rules thus generating an optimized network. The various input data and rules used to build the deterministic tree-shaped graph being uncertain, a stochastic version of the algorithm is also proposed to generate a set of probable networks in addition to the optimized one. The method is tested on the wastewater networks of Prades-le-Lez and Ramnonville Saint Agne, two towns located in Southern France. The shape and topology of the mapped networks are compared to the actual one. The results indicate an overall good agreement between the real and generated networks. The proposed algorithm may thus be used to map wastewater networks from sampled georeferenced manhole covers, elevation and street network databases.

Published
2019
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14. Mégadonnées, données liées et fouille de données pour les réseaux d'assainissement

Author

Thierry Bonnabaud La Bruyère, Nanée Chahinian, Carole Delenne, Laurent Deruelle, Mustapha Derras, Francesca Frontini, Rachel Panckhurst, Mathieu Roche, Lucile Sautot, Maguelonne Teisseire, Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environnement : Méthodes et Outils Numériques (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Berger-Levrault, Praxiling UMR 5267 (Praxiling), Université Paul-Valéry - Montpellier 3 (UM3)-Centre National de la Recherche Scientifique (CNRS), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environment: MOdels and Numerics (LEMON), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Praxiling (Praxiling), Université Paul-Valéry - Montpellier 3 (UPVM)-Centre National de la Recherche Scientifique (CNRS), Chahinian, Nanée, Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Université Paul-Valéry - Montpellier 3 (UPVM)

Subjects
[SDE] Environmental Sciences, Big Data, Mégadonnées, Réseaux d'assainissement, TALN Traitement Automatique des Langues Naturelles, Wastewater networks, [SDE]Environmental Sciences, [INFO]Computer Science [cs], [INFO] Computer Science [cs], Fouille de données, Data mining, NLP
Abstract

The “Megadata, Linked Data and Data Mining for WasteWater Networks” (MeDo) project aims to use Web big data for learning about geometry andhistory of wastewater networks, by combining different data mining techniques and multiplying analysed sources. The improved knowledge will lead to enhanced management of hydraulic heritage and water resources and allow analysis of interactions between urban development policies and water management related challenges., Le projet "Mégadonnées, données liées et fouille de données pour les réseaux d'assainissement" (MeDo) a pour objectif de tirer profit des mégadonnées disponibles sur le web pour renseigner la géométrie et l'historique d'un réseau d'assainissement, en combinant différentes techniques de fouille de données et en multipliant les sources analysées. Par l'amélioration des connaissances sur le réseau d'assainissement, ce projet contribue à une meilleure gestion du patrimoine hydraulique existant et de la ressource en eau et permettra d'analyser les interactions entre les politiques de déve-loppement urbain et les enjeux liés à la gestion de l'eau. ABSTRACT. The "Megadata, Linked Data and Data Mining for WasteWater Net-works" (MeDo) project aims to use Web big data for learning about geometry and history of wastewater networks, by combining different data mining techniques and multiplying analysed sources. The improved knowledge will lead to enhanced management of hydraulic heritage and water resources and allow analysis of interactions between urban development policies and water management related challenges.

Published
2018

15. Comparison of index systems for rating water quality in intermittent rivers

Author

Christian Salles, Jean-Louis Perrin, Nanée Chahinian, Naoual Rais, Marie-George Tournoud, Chrystelle Bancon-Montigny, Claire Rodier, Lauryan Dowse, Hydrosciences Montpellier (HSM), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), and Université Sidi Mohamed Ben Abdellah (USMBA)

Subjects
Pollution, Mediterranean climate, Water flow, media_common.quotation_subject, 0208 environmental biotechnology, Drainage basin, 02 engineering and technology, 010501 environmental sciences, Management, Monitoring, Policy and Law, 01 natural sciences, River Vene (France), Intermittent rivers, Hydrology (agriculture), Rivers, Hierarchical cluster analysis, Water Quality, Environmental monitoring, Distinguishing water quality indexes, Mediterranean and semi-arid zones, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, General Environmental Science, media_common, Hydrology, geography, geography.geographical_feature_category, Greece, Oued Fez and River Sebou (Morocco), General Medicine, Arid, 6. Clean water, 020801 environmental engineering, Morocco, 13. Climate action, [SDE]Environmental Sciences, Environmental science, Water quality, France, Desert Climate, Water Pollutants, Chemical, Environmental Monitoring
Abstract

Water quality indexes (WQI) are a practical way to evaluate and compare the level of chemical contamination of different water bodies and to spatially and temporally compare levels of pollution. The purpose of this study was to check if these indexes are appropriate for intermittent rivers under arid and semi-arid climates. A literature review enabled the comparison of 25 water quality indexes to discern their capability to evaluate spatial (inter and intra catchment) and temporal (high and low water flow conditions) variations in water quality in three Mediterranean intermittent rivers: the River Vene (France) and the Oued Fez and the River Sebou (Morocco). Hierarchical cluster analysis identified groups of WQI with similar behavior and brought to light the 6 most distinguishing indexes. Whatever the hydrological conditions at the two sites, both the ME-MCATUHE and NCS indexes, which were developed for Morocco and Greece, and the CCMEWQI and BCWQI indexes, which were developed for non-arid or semi-arid zones, gave appropriate water quality evaluations.

Published
2018
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16. Cart'Eaux: an automatic mapping procedure for wastewater networks using machine learning and data mining

Author

Carole Delenne, Nanée Chahinian, Jean-Stéphane Bailly, Sandra Bringay, Benjamin Commandre, Marc Chaumont, Mustapha Derras, Laurent Deruelle, Mathieu Roche, Fabrice Rodriguez, Gérard Subsol, Maguelonne Teisseire, Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environnement : Méthodes et Outils Numériques (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), AgroParisTech, Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-Institut de Recherche pour le Développement (IRD [ Madagascar])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Université Paul-Valéry - Montpellier 3 (UM3), ADVanced Analytics for data SciencE (ADVANSE), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Image & Interaction (ICAR), Berger-Levrault, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Eau et Environnement (IFSTTAR/GERS/EE), Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-PRES Université Nantes Angers Le Mans (UNAM), Project funded by the European Regional Development Fund and the Occitanie Region, Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Université Paul-Valéry - Montpellier 3 (UPVM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environment: MOdels and Numerics (LEMON), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Hydrosciences Montpellier (HSM), and Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[STAT.AP]Statistics [stat]/Applications [stat.AP], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [SDE.MCG]Environmental Sciences/Global Changes, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology
Abstract

International audience; In France, local government institutions must establish a detailed description of wastewater networks. The information should be available, but it remains fragmented (different formats held by different stakeholders) and incomplete. In the " Cart'Eaux " project, a multidisciplinary team, including an industrial partner, develops a global methodology using Machine Learning and Data Mining approaches applied to various types of large data to recover information in the aim of mapping urban sewage systems for hydraulic modelling. Deep-learning is first applied using a Convolution Neural Network to localize manhole covers on 5 cm resolution aerial RGB images. The detected manhole covers are then automatically connected using a tree-shaped graph constrained by industry rules. Based on a Delaunay triangulation, connections are chosen to minimize a cost function depending on pipe length, slope and possible intersection with roads or buildings. A stochastic version of this algorithm is currently being developed to account for positional uncertainty and detection errors, and generate sets of probable networks. As more information is required for hydraulic modeling (slopes, diameters, materials, etc.), text data mining is used to extract network characteristics from data posted on the Web or available through governmental or specific databases. Using an appropriate list of keywords, the web is scoured for documents which are saved in text format. The thematic entities are identified and linked to the surrounding spatial and temporal entities. The methodology is developed and tested on two towns in southern France. The primary results are encouraging: 54% of manhole covers are detected with few false detections, enabling the reconstruction of probable networks. The data mining results are still being investigated. It is clear at this stage that getting numerical values on specific pipes will be challenging. Thus, when no information is found, decision rules will be used to assign admissible numerical values to enable the final hydraulic modelling. Consequently, sensitivity analysis of the hydraulic model will be performed to take into account the uncertainty associated with each piece of information.

Published
2017

17. Automatic reconstruction of urban wastewater and stormwater networks based on uncertain manhole cover locations

Author

Benjamin Commandre, Nanée Chahinian, Jean-Stéphane Bailly, Marc Chaumont, Gérard Subsol, Fabrice Rodriguez, Mustapha Derras, Laurent Deruelle, carole delenne, Université de Montpellier (UM), Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environnement : Méthodes et Outils Numériques (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut de Recherche pour le Développement (IRD), AgroParisTech, Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-Institut de Recherche pour le Développement (IRD [ Madagascar])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Université de Nîmes (UNIMES), Image & Interaction (ICAR), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Eau et Environnement (IFSTTAR/GERS/EE), Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-PRES Université Nantes Angers Le Mans (UNAM), Berger-Levrault, Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environment: MOdels and Numerics (LEMON), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)

Subjects
EAU USEE, IMAGE AERIENNE, Wastewater and stormwater networks, Spanning tree, GESTION, VILLE, OPTIMISATION, EAU PLUVIALE, RESSOURCES EN EAU, High resolution aerial imagery, RESEAU HYDROLOGIQUE, Network mapping, AMENAGEMENT HYDRAULIQUE, [SDE]Environmental Sciences, Optimisation, CARTOGRAPHIE, METHODOLOGIE, ComputingMilieux_MISCELLANEOUS
Abstract

International audience; Accurate maps of sewer and stormwater networks in cities are mandatory for an integrated management of water resources. However, in many countries this information is unavailable or inaccurate. A new two-fold mapping method is put forward. The first step consists in using imageprocessing techniques to detect buried network surface elements such as manhole covers on very high resolution aerial imagery. The second step consists in connecting them automatically using a tree-shaped graph constrained by industry rules. The method is tested on Prades-le-Lez, Southern France. The shape and topology of the reconstructed network are compared to the actual ones.The impact of the detected objects’ density is also assessed.

Published
2017

18. LARGE-SCALE GIS-BASED URBAN FLOOD MODELLING: A CASE STUDY ON THE CITY OF OUAGADOUGOU

Author

Christophe Bouvier, Marko Adamovic, Matias Alcoba, Claire Cassé, Nanée Chahinian, Agnès Crès, Anne Crespy, Archives Nationales, Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Institut de recherches pour le développement, Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), and Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
010504 meteorology & atmospheric sciences, Scale (ratio), SYSTEME D'INFORMATION GEOGRAPHIQUE, VILLE, 0207 environmental engineering, 02 engineering and technology, 01 natural sciences, GIS-based modelling, ATHYS, Kinematic wave, Flood risks, Urban planning, African cities, 11. Sustainability, CALIBRAGE, LOGICIEL ATHYS, Drainage, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, 020701 environmental engineering, Digital elevation model, LOGICIEL D'APPLICATION, 0105 earth and related environmental sciences, Hydrology, Flood myth, Flooding (psychology), MODELISATION, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 6. Clean water, INONDATION, ETUDE DE CAS, 13. Climate action, Urban hydrology, SIMULATION, Environmental science, Surface runoff, METHODOLOGIE
Abstract

International audience; African cities are prone to recurrent flooding due to unfavorable rainfall conditions, often insufficient drainage infrastructure and fast and poorly controlled urban development. It is thus important to put forward efficient tools to characterize flooding and its consequences over large urban areas, e.g. the entire agglomeration. However, scarce data and long computation time limit the use of classic hydraulic models in such cases and require to propose alternative models. Therefore a GIS-based urban flood model is proposed as a case study in the city of Ouagadougou (Burkina Faso), in order to produce flood mapping at small spatial resolution over the entire conglomeration. Spatial discretization is done using a Digital Terrain Model (DTM) forced by various obstacles (urban blocks) or drainage axis (roads or water collectors). The mesh size is set to 10m to get a good representation of the urban objects and drainage directions. Runoff at the cell scale is calculated using an SCS model, within parameters are linked to the density of the built-up areas and to the type of soil. Runoff is first routed with a Lag and Route model over 1057 catchments which drain nearly 10 ha. The velocity of the model derives from a geomorphological formula involving the slope and the upstream area of each cell. Runoff is then routed in the main hydrological network (i.e. streets and channels) with a Kinematic Wave model. The dimensions of the cross-sections derive from a geomorphological formula, involving slope and upstream areas of the cells. An example of flooded areas is shown, as an application of the models over the entire conglomeration. The model strength resides in its quick implementation at large scale using easily accessible data, and in a fast computation time. The model appears as a valuable tool for decision-makers, real time forecasting and infrastructures management. This modelling approach may be complemented by finer, local scale models which may use the computed fluxes as boundary conditions.

Published
2017

19. Detection of manhole covers in high-resolution aerial images of urban areas by combining two methods

Author

Jérôme Pasquet, Thibault Desert, Olivier Bartoli, Carole Delenne, Mustapha Derras, Nanée Chahinian, Marc Chaumont, Gérard Subsol, Image & Interaction (ICAR), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Hydrosciences Montpellier (HSM), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université de Nîmes (UNIMES), Littoral, Environnement : Méthodes et Outils Numériques (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Berger-Levrault, Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environment: MOdels and Numerics (LEMON), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Institut de Recherche pour le Développement (IRD), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Atmospheric Science, Computer science, Feature extraction, 0211 other engineering and technologies, High resolution, 02 engineering and technology, 010502 geochemistry & geophysics, 01 natural sciences, Task (project management), Image (mathematics), Histogram, 11. Sustainability, Buried utility network, Computer vision, Limit (mathematics), Computers in Earth Sciences, Image resolution, Index Terms—Machine learning, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Signal processing, circular object detection, business.industry, high resolution, Urban sprawl, Pattern recognition, Filter (signal processing), [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], Object detection, machine learning, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Satellite, Artificial intelligence, business, geometrical filter, buried utility network
Abstract

International audience; The detection of small objects from aerial images is a difficult signal processing task. To localise small objects in an image, low-complexity geometry-based approaches can be used, but their efficiency is often low. Another option is to use appearance-based approaches that give better results but require a costly learning step. In this paper, we treat the specific case of manhole covers. Currently many manholes are not listed or are badly positioned on maps. We implement two conventional previously published methods to detect manhole covers in images. The first one searches for circular patterns in the image while the second uses machine learning to build a model of manhole covers. The results show non optimal performances for each method. The two approaches are combined to overcome this limit, thus increasing the overall performance by about forty percent.

Published
2016
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20. Flow and nutrient transport in intermittent rivers: a modelling case-study on the Vène River using SWAT 2005

Author

Marie-George Tournoud, Nanée Chahinian, Bernadette Picot, and Jean-Louis Perrin

Subjects
010504 meteorology & atmospheric sciences, Soil and Water Assessment Tool, Scale (ratio), Flow (psychology), 0207 environmental engineering, Drainage basin, QUALITE DE L'EAU, 02 engineering and technology, SEDIMENT, 01 natural sciences, VALIDATION, MODELE HYDROLOGIQUE, AZOTE, DEBIT SOLIDE, CALAGE, SWAT model, 020701 environmental engineering, 0105 earth and related environmental sciences, Water Science and Technology, Hydrology, geography, geography.geographical_feature_category, PHOSPHORE, BASSIN EXPERIMENTAL, Sediment, NUTRIMENT, 6. Clean water, DEBIT, COURS D'EAU, ETUDE DE CAS, SIMULATION, HYDRODYNAMIQUE, MODELE SWAT, Environmental science, CRUE, Water quality, TRANSPORT SOLIDE, POLLUTION DES EAUX CONTINENTALES, Sediment transport
Abstract

Intermittent rivers have a specific hydrological behaviour which also influences water quality dynamics. The objective of this work was to model the flow and water quality dynamics of a coastal Mediterranean intermittent river using the Soil and Water Assessment Tool (SWAT 2005). Flow, sediment, nitrogen and phosphorus transport were simulated on the Vene experimental catchment, France. The model was sequentially calibrated at sub-catchment scale and validated both at sub-catchment and catchment scales. A procedure for building the data records for the point sources is presented. The results indicate that, while the model produces good results for flow simulation, its performance for sediment transport is less satisfactory. This in turn impacts on the nutrient transport module. The reasons behind these shortcomings are analysed, taking into account the length of the data records, their distribution and the equations used in the SWAT model. The need for a thorough multi-objective model validation ...

Published
2011
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21. Manhole cover detection using a geometrical filter on very high resolution aerial and satellite images

Author

Marie-George Tournoud, Aude Allard, Jean-Stéphane Bailly, Olivier Bartoli, Christian Salles, Fabrice Rodriguez, Carole Delenne, Nanée Chahinian, Katia Chancibault, Hydrosciences Montpellier (HSM), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Eau et Environnement (IFSTTAR/GERS/EE), Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-PRES Université Nantes Angers Le Mans (UNAM), Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'étude des interactions entre sols, agrosystèmes et hydrosystèmes (LISAH), and Institut National de la Recherche Agronomique (INRA)

Subjects
Very high resolution, 010504 meteorology & atmospheric sciences, Cover (telecommunications), Computer science, IMAGE, DRAINAGE, RESEAU DE DRAINAGE, ASSAINISSEMENT, 02 engineering and technology, 01 natural sciences, DETECTION, TRAITEMENT DU SIGNAL, [SPI]Engineering Sciences [physics], 0202 electrical engineering, electronic engineering, information engineering, DETECTION D'OBJET, Drainage network, Computer vision, TRAITEMENT DES IMAGES, TELEDETECTION, Image resolution, SATELLITE, 0105 earth and related environmental sciences, Remote sensing, business.industry, Orthophoto, Filter (video), Utility network, FILTRE, EGOUT, 020201 artificial intelligence & image processing, Satellite, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, DETECTION DES CONTOURS
Abstract

JURSE 2015, Joint Urban Remote Sensing Event, LAUSANNE, SUISSE, 30-/03/2015 - 01/04/2015; One of the direct consequences of urban growth is the multiplication of buried utility networks. It is difficult to obtain complete and accurate maps of these networks because they have often been produced by different parties at different times. This work aims at putting forward a methodology to detect manhole covers on high resolution images, in order to reconstruct an urban drainage network. A circular detection filter is applied to high resolution orthophotos and Pleiade images to pinpoint mahole covers. The primary results are encouraging as 42% of manhole covers are detected by this method. Further work is carried out to improve the method and extend it to rectangular grates, that are not detected by the circular filter.; Détection automatique de regards du réseau d'assainissement par analyse d'images satellites

Published
2015
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22. Assessing the impact of the hydraulic properties of a crusted soil on overland flow modelling at the field scale

Author

Nanée Chahinian, Gwenn Trotoux, Marc Voltz, Roger Moussa, Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], 0207 environmental engineering, 02 engineering and technology, 01 natural sciences, HORTONIAN RUNOFF, OVERLAND FLOW, Hydraulic conductivity, Pedotransfer function, 020701 environmental engineering, Subsoil, 0105 earth and related environmental sciences, Water Science and Technology, PLOT SCALE, Hydrology, HYDROLOGIE, 15. Life on land, 6. Clean water, Soil gradation, Infiltration (hydrology), SOIL HYDRAULIC PROPERTIES, INFILTRATION, SOL EN CROUTE, [SDE]Environmental Sciences, Soil water, Environmental science, Richards equation, CRUST, INVERSE MODELLING, Surface runoff
Abstract

Correspondance: chahinia@ensam.inra.fr; International audience; Soil surface crusts are widely reported to favour Hortonian runoff, but are not explicitly represented in most rainfall-runoff models. The aim of this paper is to assess the impact of soil surface crusts on infiltration and runoff modelling at two spatial scales, i.e. the local scale and the plot scale. At the local scale, two separate single ring infiltration experiments are undertaken. The first is performed on the undisturbed soil, whereas the second is done after removal of the soil surface crust. The HYDRUS 2D two-dimensional vertical infiltration model is then used in an inverse modelling approach, first to estimate the soil hydraulic properties of the crust and the subsoil, and then the effective hydraulic properties of the soil represented as a single uniform layer. The results show that the crust hydraulic conductivity is 10 times lower than that of the subsoil, thus illustrating the limiting role the crust has on infiltration. Moving up to the plot scale, a rainfall-runoff model coupling the Richards equation to a transfer function is used to simulate Hortonian overland flow hydrographs. The previously calculated hydraulic properties are used, and a comparison is undertaken between a single-layer and a double-layer representation of the crusted soil. The results of the rainfall-runoff model show that the soil hydraulic properties calculated at the local scale give acceptable results when used to model runoff at the plot scale directly, without any numerical calibration. Also, at the plot scale, no clear improvement of the results can be seen when using a double-layer representation of the soil in comparison with a single homogeneous layer. This is due to the hydrological characteristics of Hortonian runoff, which is triggered by a rainfall intensity exceeding the saturated hydraulic conductivity of the soil surface. Consequently, the rainfall-runoff model is more sensitive to rainfall than to the subsoil's hydrodynamic properties. Therefore, the use of a double-layer soil model to represent runoff on a crusted soil does not seem necessary, as the increase of precision in the soil discretization is not justified by a better performance of the model.

Published
2006
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23. Les processus hortoniens et leur importance dans la genèse et le développement des crues en zones semi-arides

Author

Roger Moussa, Nanée Chahinian, and Jean Albergel

Subjects
Mediterranean climate, Hydrology, Hydraulic conductivity, TRACER, Soil water, Tropics, Surface runoff, Saturation (chemistry), Water content, Geology, Water Science and Technology
Abstract

Hortonian overland flow by rainfall excess seems to be the main runoff process in semi-arid areas as demonstrated directly through observations and measurements of rainfall-runoff events, and indirectly from isotopic, geochimical and biological tracer tests. Examples taken from semi-arid tropical zone (Burkina Faso and Senegal) and the Mediterranean zone (Tunisia and Southern France) illustrate this hypothesis. Several spatial scales, varying from few square meters (m 2 ) for rainfall simulations, to plots of hundreds of m 2 and small catchments of few km 2 are studied. Horton's empirical model is applied and validated at various spatial scales. The model is suitable for crusted soils and for events where the hydraulic conductivity at saturation of the soil surface is low compared to rainfall intensity. Simple relationships are used to relate horton's model prameters to the hydraulic conductivity at natural saturation and initial water content.

Published
2003
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24. Temporal and spatial variability of organotins in an intermittent Mediterranean river

Author

Christian Salles, Jean Louis Perrin, C. Rodier, F. Hernandez, J.L. Seidel, Chrystelle Bancon-Montigny, V. Brunel, E. Gayrard, P. Marchand, M. G. Tournoud, G. Aubert, Nanée Chahinian, Laboratoire Physique des Surfaces Naturelles et Génie Rural, AGROCAMPUS OUEST, Service de Physique Théorique (SPhT), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS), Génétique et Ecophysiologie des Légumineuses à Graines (UMRLEG) (UMR 102), Etablissement National d'Enseignement Supérieur Agronomique de Dijon (ENESAD)-Institut National de la Recherche Agronomique (INRA)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Laboratoire de Synthèse et Fonctionnalisation de Céramiques (UMR 3080), Saint Gobain, Synchrotron SOLEIL (SSOLEIL), Centre National de la Recherche Scientifique (CNRS), Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique Corpusculaire - Clermont-Ferrand (LPC), Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), and Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Mediterranean climate, Environmental Engineering, 010504 meteorology & atmospheric sciences, Stabilization pond, Stormwater, Sewage, QUALITE DE L'EAU, 010501 environmental sciences, Management, Monitoring, Policy and Law, 01 natural sciences, Waste Disposal, Fluid, FACTEUR ANTHROPIQUE, VARIATION TEMPORELLE, Spatio-Temporal Analysis, Rivers, Tributary, Organotin Compounds, BUTYLETAIN, [CHIM]Chemical Sciences, Waste Management and Disposal, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, Pollutant, Hydrology, geography, TOXICITE, geography.geographical_feature_category, business.industry, General Medicine, 6. Clean water, ORGANOETAIN, COURS D'EAU, OCTYLETAIN, Biodegradation, Environmental, Water Framework Directive, 13. Climate action, VARIATION SPATIALE, [SDE]Environmental Sciences, Environmental science, Spatial variability, France, business, POLLUTION DES EAUX CONTINENTALES, Water Pollutants, Chemical, Environmental Monitoring
Abstract

Organotin compounds (OTs) are exclusively anthropogenic and have been widely used for their biocidal properties and as stabilizers in various industrial applications. Hence organotins are common pollutants. Their high toxicity has led to their entry on the EU water framework's priority substances' list. However, few studies are available regarding their behaviour in surface waters, in particular, in intermittent Mediterranean rivers. The Vene is an intermittent river located in Languedoc-Roussillon, southern France. It is the main tributary of an important shellfish farming site: the Thau lagoon. The present study aims at establishing the presence of OTs on a 1.5 km long reach of the river into which a waste water treatment plant (WWTP) discharges. The study is carried out during steady-state flow conditions over two consecutive years and investigates potential OTs sources in everyday domestic activities. Routine field monitoring was carried out over a 5 month period during the springs of 2008 and 2009. The results establish the presence of butyltins and octyltins throughout the 1.5 km long reach at concentrations exceeding the maximum allowable concentration levels imposed by the water framework directive. The WWTP is recognized as an important OTs point source; however, using trace and rare earth elements as tracers, an urban stormwater sewage gutter is identified as a secondary source. Its impact on the river's pollutant loads is however variable in time because of flow intermittency. The paper discusses the need for specific monitoring and management schemes for intermittent rivers.

Published
2013
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25. The role of river sediments in contamination storage downstream of a waste water treatment plant in low flow conditions: Organotins, faecal indicator bacteria and nutrients

Author

Jean-Louis Perrin, David Rosain, Claire Rodier, Patrice Got, Chrystelle Bancon-Montigny, Audrey Caro, Marie-George Tournoud, Nanée Chahinian, Bernadette Picot, Hydrosciences Montpellier (HSM), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), MARine Biodiversity Exploitation and Conservation (UMR MARBEC), and Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Pollution, 010504 meteorology & atmospheric sciences, media_common.quotation_subject, 010501 environmental sciences, Aquatic Science, Oceanography, water quality, 01 natural sciences, priority substances, Point source pollution, Nutrient, anthropogenic pollution, 11. Sustainability, Tributary, [CHIM]Chemical Sciences, Thau lagoon (France), Effluent, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, media_common, Pollutant, Hydrology, geography, geography.geographical_feature_category, sediment-water column interactions, Sediment, river-coastal lagoon continuum, 6. Clean water, 13. Climate action, [SDE]Environmental Sciences, Environmental science, Water quality
Abstract

In intermittent rivers, characterised by a specific hydrological behaviour, sediment–water column interactions may influence water quality during low flows. The main objective of this work was to assess the extent of anthropogenic pollution (organotins, faecal indicator bacteria and nutrients) in the river sediment of an intermittent river and its impact on the downstream water bodies: The Vene River, main tributary of the Thau lagoon. We first investigated anthropogenic pollution from water and sediment samples collected in situ along the river course (1.5 km); then, during laboratory experiments, we assessed the survival of faecal bacteria and quantified the degradation rates of organotins on the same sediments. The results indicate the presence of anthropogenic pollution all along the study reach. The waste water treatment plant effluent is a direct pollution input source for anthropogenic pollution. The sediment data points to an urban drainage ditch as a secondary point pollution source while the organotins data highlights the presence of other diffuse sources, specific to this substance. The results of the laboratory experiments show that both faecal bacteria and organotins may persist in the river sediments for up to two months in summer and even longer in winter. This indicates that sediments may, under favourable conditions, become important pollutant stores which may later be released and transported to the Thau lagoon during floods.

Published
2012
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26. Distributed hydrological modelling of a Mediterranean mountainous catchment - Model construction and multi-site validation

Author

Nanée Chahinian, Roger Moussa, Claude Bocquillon, Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Université Montpellier 2 - Sciences et Techniques (UM2)

Subjects
MODEL CALIBRATION AND VALIDATION, 010504 meteorology & atmospheric sciences, Correlation coefficient, Hydrological modelling, [SDV]Life Sciences [q-bio], 0207 environmental engineering, 02 engineering and technology, 01 natural sciences, Interflow, Water balance, Evapotranspiration, MEDITERRANEAN MOUNTAINOUS CATCHMENT, 020701 environmental engineering, Digital elevation model, 0105 earth and related environmental sciences, Water Science and Technology, Hydrology, Base flow, HYDROLOGIE, 6. Clean water, 13. Climate action, [SDE]Environmental Sciences, Environmental science, PARAMETERISATION, SPATIALLY DISTRIBUTED HYDROLOGICAL MODEL, Surface runoff, MULTI-SITE APPROACH
Abstract

Corresponding author. fax: +33 4 67 63 26 14. E-mail Addresses: moussa@ensam.inra.fr; International audience; A multi-site validation approach is necessary to further constrain distributed hydrological models. Such an approach has been tested on the Gardon catchment located in the mountainous Mediterranean zone of southern France using data gathered over a 10 year period on nine internal subcatchments. A spatially distributed hydrological model linked to a Geographical Information System, was developed on the basis of simplified physical process representations (infiltration, evapotranspiration, base flow, interflow,overland flow, channel routing), using conventional hydro-meteorological data and readily accessible geographical maps. The model. parameters were estimated from a Digital Elevation Model, soil and land-use maps; and only five parameters were calibrated for the whole catchment. Three procedures with different levels of calibration and validation were conducted at a daily time step, and the results of both calibration and validation were compared on the basis-of their performance with regards to objective criteria representing the water balance, the Nash and Sutcliffe efficiency and the correlation coefficient. The first application corresponds to the case of an ungauged catchment i.e. a simple application of the model without calibration. In the second application, the model. was calibrated using discharge values measured at the outlet on the first five year period and validated using data from intermediate gauging stations and on the remaining period at the outlet. In the third application, a multi-site calibration and validation was conducted simultaneously for all available stations using the first five year period and validated on the second five year period for all stations. Calibration against the outlet station and internal validation against eight additional stations revealed some short-comings for a few upstream tributaries. Further calibration against additional discharge stations improved the model's performance at the subcatchment level. These different calibration and validation tests challenge the predictive capability of the model both at the catchment and subcatchment level and hence illustrate the model's possible improvements (structure, data and parameterisation strategy) for predictions on ungauged catchments

Published
2007
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27. Catalogue of the models used in MOPEX 2004/2005

Author

Andreassian, V., Bergström, S., Nanée Chahinian, Duan, Q., Littlewood, I., Mathevet, T., Michel, C., Andrea Montanari, Moretti, G., Roger Moussa, Nasonova, O. N., Connor, K. O., Paquet, E., Perrin, C., Rousseau, A., Schaake, J., Wagener, T., Xie, Z., Hydrosystèmes et Bioprocédés (UR HBAN), Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), SMHI SWE, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Institut de Recherche pour le Développement (IRD), and LLNL USA

Subjects
VIC, SWAP, NOAH, RRMT, MOPEX, SMAR, SAC-SMA, AFFDEF, SWB, HYDROTEL, [SDE]Environmental Sciences, HBV, GR4J, GR4H, MORDOR, MODSPA, IHACRES
Abstract

International audience; The following section presents a synthetic description of the models that were used in the various articles of this Red Book. The following models are covered (descriptions can be found in alphabetical order) : AFFDEF, GR4H, GR4J, HBV, HYDROTEL, IHACRES, MODSPA, MORDOR, NOAH, RRMT, SAC-SMA, SMAR, SWAP, SWB, VIC.

Published
2006

28. Accounting for temporal variation in soil hydrological properties when simulating surface runoff on tilled plots

Author

Nanée Chahinian, Marc Voltz, Roger Moussa, Patrick Andrieux, Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
STORMFLOW GENERATION, Hydraulics, [SDV]Life Sciences [q-bio], 0207 environmental engineering, Hydrograph, Soil science, 02 engineering and technology, FARMED CATCHMENTS, law.invention, FLOOD EVENT, Hydraulic conductivity, law, GENERATION DES CRUES, 020701 environmental engineering, RUNOFF MODEL, Water Science and Technology, Hydrology, PLOT SCALE, TILLAGE, CALIBRATION, HYDROLOGIE, 04 agricultural and veterinary sciences, 6. Clean water, Runoff model, Tillage, Infiltration (hydrology), INFILTRATION, Soil water, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Surface runoff
Abstract

Correspondance: fax: +33 4 6763 2614. E-mail address: moussa@ensam.inra.fr; International audience; Tillage operations are known to greatly influence local overland flow, infiltration and depressional storage by altering soil hydraulic properties and soil surface roughness. The calibration of runoff models for tilled fields is not identical to that of unfilled fields, as it has to take into consideration the temporal variability of parameters due to the transient nature of surface crusts. In this paper, we seek the application of a rainfall-runoff model and the development of a calibration methodology to take into account the impact of tillage on overland flow simulation at the scale of a tilled plot (3240 m(2)) located in southern France. The selected model couples the (Morel-Seytoux, H.J., 1978. Derivation of equations for variable rainfall infiltration. Water Resources Research. 14(4), 561-568). Infiltration equation to a transfer function based on the diffusive wave equation. The parameters to be calibrated are the hydraulic conductivity at natural saturation K-s, the surface detention S-d and the lag time omega. A two-step calibration procedure is presented. First, eleven rainfall-runoff events are calibrated individually and the variability of the calibrated parameters are analysed. The individually calibrated K-s values decrease monotonously according to the total amount of rainfall since tillage. No clear relationship is observed between the two parameters S-d and omega, and the date of tillage. However, the lag time omega increases inversely with the peakflow of the events. Fairly good agreement is observed between the simulated and measured hydrographs of the calibration set. Simple mathematical laws describing the evolution of K-s and omega are selected, while S-d is considered constant. The second step involves the collective calibration of the law of evolution of each parameter on the whole calibration set. This procedure is calibrated on 11 events and validated on ten runoff inducing and four non-runoff inducing rainfall events. The suggested calibration methodology seems robust and can be transposed to other gauged sites.

Published
2006
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29. Estimation of rainfall-runoff model parameters based on a large sample of basin characteristics

Author

Bourqui, M., Plantier, M., Loumagne, C., Andreassian, V., Nanée Chahinian, Hydrosystèmes et Bioprocédés (UR HBAN), Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), DAF GUYANE, Partenaires IRSTEA, and Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)

Subjects
[SDE]Environmental Sciences
Abstract

Estimation of model parameters has been a major problem since the last two decades and if we want to address this problem in the context of ungauged basins it becomes much more challenging. We might think that technological progress in collecting spatialized data (Geographic Information Systems (GIS), Digital Elevation Models (DEM), radars~) offers the possibility to take into account the spatial variability of rainfall and watersheds physical characteristics, in rainfall-runoff models without carrying out any measurement in the field. To use this information, the lumped approach can be easily replaced by the semi-distributed approach. But, does the integration of additional information in a semi-distributed approach effectively improve the performance of flow simulations of this type of models? Moreover, which kind of heterogeneities are to be taken into account to make significant improvements ? In this paper, we deal with the impacts of using physical heterogeneities of basins on the performances of lumped and semi-distributed approaches. Given the requirement of a large database to produce statistically significant results, chimeras basins were used. Chimera basins are built by gathering two real but physically distant basins. Thus, we obtain a large sample of artificial catchments having real rainfall and runoff data at the scale of each real sub-basin.

Published
2005

30. Prise en compte des variabilités spatio-temporelles dans les modèles pluie-débit pour une meilleure prévision des crues

Author

Loumagne, C., Bourqui, M., Saulnier, G. M., Lelay, M., Roger Moussa, Nanée Chahinian, Andreassian, V., Irstea Publications, Migration, Hydrosystèmes et Bioprocédés (UR HBAN), Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), Laboratoire d'étude des transferts en hydrologie et environnement (LTHE), Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
[SDE] Environmental Sciences, MODÈLES PLUIE-DÉBIT, GR4, [SDE]Environmental Sciences, TOPMODEL, APPROCHES GLOBALES ET SEMI-DISTRIBUÉES, VARIABILITÉS SPATIO-TEMPORELLES
Abstract

Dans le contexte opérationnel de gestion des ressources et de prévision des crues, nous avons cherché à intégrer des données spatialisées les plus pertinentes à l'échelle du bassin en vue d'améliorer la performance des modèles pluie-débit. Cette recherche nous a permis de confirmer que la prise en compte des variabilités spatiales du bassin versant permet d'améliorer les simulations des débits à son exutoire. En effet, à condition de distribuer les pluies, l'approche semi-distribuée appliquée à 3 300 bassins chimères a montré une performance moyenne supérieure à l'approche globale. En moyennant les critères de performance pour des groupes de bassins d'hétérogénéités similaires, nous avons pu distinguer certaines tendances telles que celles concernant l'indice hypsométrique, l'indice de réponse du bassin ou certains indices de couvert végétal. Une comparaison a été effectuée entre le modèle GR4 global et TOPMODEL modèle semi-distribué afin d'analyser la dépendance existante entre la structure interne du modèle et ses performances en simulation des débits. Une autre comparaison entre le modèle global GR4J et le modèle distribué ModSpa, a permis d'étudier l'évolution des performances de l'approche spatialisée lorsqu'on augmente le nombre de sous-bassins. Par ailleurs, l'existence d'une corrélation directe entre les descripteurs de forme du réseau et les caractéristiques de l'hydrogramme unitaire géomorphologique ont permis d'identifier une fonction de transfert à partir de la topographie. Il serait par la suite intéressant d'approfondir l'intégration de la pluie par une prise en compte de la variabilité au pas de temps horaire ainsi que l'intégration d'une fonction de transfert géomorphologique dans le modèle global pour augmenter ses performances.

Published
2005

31. Comparison of infiltration models to simulate flood events at the field scale

Author

Nanée Chahinian, Marc Voltz, Roger Moussa, Patrick Andrieux, Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
Calibration and validation, 010504 meteorology & atmospheric sciences, Mean squared error, [SDV]Life Sciences [q-bio], 0207 environmental engineering, Hydrograph, 02 engineering and technology, 01 natural sciences, Transfer function, FIELD SCALE, Statistics, RUNOFF, 020701 environmental engineering, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, Water Science and Technology, Hydrology, Flood myth, 6. Clean water, INFILTRATION MODEL, Infiltration (hydrology), 13. Climate action, MODEL CALIBRATION, [SDE]Environmental Sciences, Environmental science, Mathematical structure, Surface runoff
Abstract

Most runoff simulation and infiltration models have been developed at the global-catchment scale or the local-soil column scale. Few models have been specifically developed at the scale of agricultural fields and there are no guidelines to help modellers choose an adequate model to simulate overland flow and hence analyse the impact of different soil management practices on flood generation. A comparison is undertaken to select and calibrate models that simulate Hortonian overland flow at the field or small plot scale. The proposed methodology couples a runoff production model to a unit hydrograph transfer function. Four different models were tested: Philip, Morel-Seytoux, Horton and SCS. These models differ by their mathematical structure and the parameters to be calibrated while input hydrologic data are the same site data: rainfall/runoff and initial water content. The models are calibrated on 14 events and validated on 14 others. The results of both the calibration and validation phases are compared on the basis of their performance with regards to six objective criteria, three global criteria and three relative criteria representing volume, peakflow, and the root mean square error. The first type of criteria gives more weight to strong events whereas the second considers all events to be of equal weight. The results show that the calibrated parameter values are dependent on the type of objective criteria used. Furthermore, when analysing the performance of the six objective criteria used, it can be seen that the global volume, global RMSE and relative peakflow criteria give the best compromise between bias and precision. Within the selected modelling framework, Morel-Seytoux's model performed better than the other three and the SCS gave the worst results. Horton's model showed to be more consistent in overall performance than Philip's model. Results also highlight problems related to the simulation of low flow events and intermittent rainfall events.

Published
2005
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34. How can big data be used to reduce uncertainty in stormwater modelling?

Author

Nanée Chahinian, Anne-Laure Piat-Marchand, Sandra Bringay, Maguelonne Teisseire, Elodie Boulogne, Laurent Deruelle, Mustapha Derras, carole delenne, Hydrosciences Montpellier (HSM), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), ADVanced Analytics for data SciencE (ADVANSE), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Université Paul-Valéry - Montpellier 3 (UPVM), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Berger-Levrault, Littoral, Environnement : Méthodes et Outils Numériques (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Projet Cart'Eaux (fonds européens FEDER et région Languedoc-Roussillon), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environment: MOdels and Numerics (LEMON), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), and Université Paul-Valéry - Montpellier 3 (UM3)

Subjects
UNCERTAINTY ANALYSIS, BIG DATA, NETWORK CHARACTERISTICS, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], NETWORK ELEMENT, STORMWATERS, BURIED UTILITIES
Abstract

[Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE [Axe_IRSTEA]TETIS-SISO; International audience; Urban growth is an ongoing trend and one of its direct consequences is the development of buried utility networks. With growing needs among consumers, new networks are being in- stalled and more underground space is being occupied. Locating these networks is becoming a challenging task. Mispositioning of utility networks is an important problem for both indus- trialised and developing countries and will worsen as cities expand and their networks increase in size and complexity (Jamil et al. (2012), Metje et al. (2007)). Over the past century, it was common practice for public service providers to install, operate and repair their networks sepa- rately Rogers et al. (2012). Now local authorities are confronted with the task of combing data produced by different parties, having distinct formats, variable precision and granularity (Chen and Cohn (2011)). Although in certain countries contractors are now obliged by law to position all buried networks within set precision ranges, finding data related to older network branches is a cumbersome task. Once located these data are often unavailable at the desired precision or are prone to errors or omissions. This study is a part of a global project which aims to recreate a storm water and a sewage network in settings where no accurate information regarding the position or characteristics of buried utility networks is available. The methodology consists in detecting objects, such as manhole covers or inlet grates, from areal photographs and very high resolution satellite imagery and use alternative sources of big data in order to extract interesting descriptor about them. The big data is original information scrapped from the internet such as calls for tenders, newspaper articles, consumer complaints etc. Information extracted with text mining techniques such as used in Kergosien et al. (2015) are particularly interesting to confirm or infirm the position of the previously detected manhole covers and inlet grate. This infor- mation is then used to build an attribute table of the underlying pipes. Once located, standard industry recommendations for pipe selection (diameter, slope, depth, junctions, etc.) are used to define a statistically probable network, including uncertainty associated to each characteristic. The final objective of this work will be to carry out hydraulic simulations using a classical mod- elling software and assess the output hydrographs sensitivity to location and descriptor errors.

35. Détection de regards de visite sur des images à haute résolution spatiale par une méthode d'apprentissage

Author

Benjamin Commandré, Driss En-Nejjary, Lionel Pibre, Marc CHAUMONT, Gérard Subsol, Laurent Deruelle, Mustapha Derras, Carole Delenne, Nanée Chahinian, Université de Montpellier (UM), Image & Interaction (ICAR), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Berger-Levrault, Littoral, Environnement : Méthodes et Outils Numériques (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Hydrosciences Montpellier (HSM), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environment: MOdels and Numerics (LEMON), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), and Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Cartographie, Réseaux d'assainissement, Réseaux de neurones, Très haute résolution, [SDE.ES]Environmental Sciences/Environmental and Society
Abstract

National audience; L’utilisation de la télédétection en milieux urbains s’est focalisée dans un premier temps sur l’identification et l’estimation des surfaces urbaines (Weng, 2011), la mise en évidence de l’expansion urbaine (Xian et Crane, 2005), la quantification du microclimat (Arthur-Harthnaft et al., 2001) ou de l’îlot de chaleur urbain (Dousset et Gourmelon, 2004; Gallo et al., 1994).En combinant les approches multi-spectrales et multi-temporelles, des suivis ont ainsi pu être faits sur l’évolution de ces phénomènes. A mesure que la résolution spatiale des images s’améliorait, l’identification d’objets précis du paysage urbain devenait possible (Bhaskaran et al., 2010). Ainsi, depuis quelques années, les images sont utilisées pour détecter les réseaux routiers (Péteri et Ranchin, 2006, Stoica et al. 2004), la végétation urbaine (Höfle, 2012), les caractéristiques du bâti (Hermosilia et al., 2011; Freire et al., 2010; Lhomme et al., 2004). En comparaison, la localisation d'objets de plus petite dimension (inférieure à un immeuble) reste relativement peu étudiée et pose le problème de l'identification d'objets dans un environnement variable, sur fond non-uniforme et souvent peu contrasté. Dans le cadre du projet Cart’eaux*, nous proposons une méthode de détection de regards de visite (plaques d’égout) sur des images à très haute résolution spatiale. Cette méthode par apprentissage consiste à appliquer un réseau de neurones à convolution, AlexNet, sur des orthophotos à très haute résolution spatiale prises dans le visible. Le réseau est entrainé sur une image à 5cm de résolution de la ville de Prades-Le-Lez dans l’Hérault. Afin de pallier le manque de données – seulement 605 plaques d’égout présentes sur l’image – une procédure d’augmentation de la base d’apprentissage est appliquée : rotation, miroir… conduisant à une base d’apprentissage à de 2 classes constituée de 18405 « plaques d’égout » et 458915 « autres objets ». Une fois le réseau entrainé et « boosté », il est appliqué sur la ville de Gigean avec une précision de 72% et un rappel de 54%.Plusieurs pistes de recherche sont actuellement explorées afin d’améliorer ces résultats déjà encourageants. D’une part, l’utilisation d’une information altimétrique (MNT, LIDAR) ou de données géographiques (bâtis) permettrait de réduire le nombre de faux positifs (e.g. cheminées) ; d’autre part l’application d’un filtre géométrique en amont de la classification fournira une information supplémentaire au réseau de neurones qui devrait augmenter le nombre de vrais positifs. * Projet financé par la région Occitanie et l’Europe (fond FEDER)

36. WEIR-P: An Information Extraction Pipeline for the Wastewater Domain

Author

Rachel Panckhurst, Mathieu Roche, Laurent Deruelle, Marin Julien, Maguelonne Teisseire, Lucile Sautot, Carole Delenne, Thierry Bonnabaud La Bruyère, Francesca Frontini, Nanée Chahinian, Hydrosciences Montpellier (HSM), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Istituto di Linguistica Computazionale 'Antonio Zampolli' (CNR-ILC), Consiglio Nazionale delle Ricerche [Roma] (CNR), Littoral, Environnement : Méthodes et Outils Numériques (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire de linguistique diachronique, de sociolinguistique et de didactique des langues (DIPRALANG), Université Paul-Valéry - Montpellier 3 (UPVM), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Département Environnements et Sociétés (Cirad-ES), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Berger-Levrault, Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire de sociolinguistique, d’anthropologie des pratiques langagières et de didactique des langues-cultures (DIPRALANG), and Supported by the Occitanie-Pyrenees-Mediterrannee Region under grant 2017006570/DF-000014.

Subjects
Text mining, Information extraction, Computer science, 0211 other engineering and technologies, 02 engineering and technology, Wastewater, Reconnaissance d'Entités Nommées (REN), computer.software_genre, NLP, Domain (software engineering), Annotation, TALN Traitement Automatique des Langues Naturelles, 0202 electrical engineering, electronic engineering, information engineering, Domain adapted systems, [INFO]Computer Science [cs], [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, Adaptation (computer science), 021101 geological & geomatics engineering, Information retrieval, Réseaux d'assainissement, Extraction d'information IE, Conceptual model (computer science), [SCCO.LING]Cognitive science/Linguistics, Pipeline (software), 6. Clean water, NER, Domain knowledge, 020201 artificial intelligence & image processing, State (computer science), computer, Fouille de données textuelles
Abstract

International audience; We present the MeDO project, aimed at developing resourcesfor text mining and information extraction in the wastewater domain.We developed a specific Natural Language Processing (NLP) pipelinenamed WEIR-P (WastewatEr InfoRmation extraction Platform) which identifies the entities and relations to be extracted from texts, pertaining to network information, wastewater treatment, accidents and works,organizations, spatio-temporal information, measures and water quality. We present and evaluate the first version of the NLP system which was developed to automate the extraction of the aforementioned annotationfrom texts and its integration with existing domain knowledge. The preliminary results obtained on the Montpellier corpus are encouraging and show how a mix of supervised and rule-based techniques can be used to extract useful information and reconstruct the various phases of theextension of a given wastewater network. While the NLP and Information Extraction (IE) methods used are state of the art, the novelty of our work lies in their adaptation to the domain, and in particular in the wastewater management conceptual model, which defines the relations between entities. French resources are less developed in the NLP community than English ones. The datasets obtained in this project are another original aspect of this work.

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